基于递推闭环子空间辨识的自适应预测控制方法

苏奇全, 贾宏光, 朱明超, 刘慧, 宣明

苏奇全, 贾宏光, 朱明超, 刘慧, 宣明. 基于递推闭环子空间辨识的自适应预测控制方法[J]. 信息与控制, 2015, 44(2): 252-256. DOI: 10.13976/j.cnki.xk.2015.0252
引用本文: 苏奇全, 贾宏光, 朱明超, 刘慧, 宣明. 基于递推闭环子空间辨识的自适应预测控制方法[J]. 信息与控制, 2015, 44(2): 252-256. DOI: 10.13976/j.cnki.xk.2015.0252
SU Qiquan, JIA Hongguang, ZHU Mingchao, LIU Hui, XUAN Ming. An Adaptive Predictive Control Method Based on Recursive Closed-loop Subspace Identification[J]. INFORMATION AND CONTROL, 2015, 44(2): 252-256. DOI: 10.13976/j.cnki.xk.2015.0252
Citation: SU Qiquan, JIA Hongguang, ZHU Mingchao, LIU Hui, XUAN Ming. An Adaptive Predictive Control Method Based on Recursive Closed-loop Subspace Identification[J]. INFORMATION AND CONTROL, 2015, 44(2): 252-256. DOI: 10.13976/j.cnki.xk.2015.0252
苏奇全, 贾宏光, 朱明超, 刘慧, 宣明. 基于递推闭环子空间辨识的自适应预测控制方法[J]. 信息与控制, 2015, 44(2): 252-256. CSTR: 32166.14.xk.2015.0252
引用本文: 苏奇全, 贾宏光, 朱明超, 刘慧, 宣明. 基于递推闭环子空间辨识的自适应预测控制方法[J]. 信息与控制, 2015, 44(2): 252-256. CSTR: 32166.14.xk.2015.0252
SU Qiquan, JIA Hongguang, ZHU Mingchao, LIU Hui, XUAN Ming. An Adaptive Predictive Control Method Based on Recursive Closed-loop Subspace Identification[J]. INFORMATION AND CONTROL, 2015, 44(2): 252-256. CSTR: 32166.14.xk.2015.0252
Citation: SU Qiquan, JIA Hongguang, ZHU Mingchao, LIU Hui, XUAN Ming. An Adaptive Predictive Control Method Based on Recursive Closed-loop Subspace Identification[J]. INFORMATION AND CONTROL, 2015, 44(2): 252-256. CSTR: 32166.14.xk.2015.0252

基于递推闭环子空间辨识的自适应预测控制方法

基金项目: 中国科学院知识创新工程国防科技创新重要项目(YYYJ-1122)
详细信息
    作者简介:

    苏奇全(1988-),男,硕士生.研究领域为自适应控制.
    贾宏光(1971-),男,研究员,博士生导师.研究领域为复合制导及目标识别技术.

    通讯作者:

    苏奇全,suqq12345@163.com

  • 中图分类号: TP273

An Adaptive Predictive Control Method Based on Recursive Closed-loop Subspace Identification

  • 摘要: 针对存在噪声干扰与时变特性的线性系统的模型不确定性问题,提出了一种基于递推闭环子空间辨识的自适应预测控制方法. 通过结合PID(proportional-integral-derivative)控制采用新的目标函数,对闭环子空间预测控制算法进行改进,推导出具有类似PID结构的闭环子空间预测控制算法;采用固定输入输出数据集大小的递推方法将改进后的算法在线实施,通过采用一种简单直观的更新方法代替LQ分解,有效提高了在线计算效率. 最后,通过仿真实验验证了方法的有效性.
    Abstract: In order to deal with model uncertainty problem for linear systems with time-variability, which exists due to noise perturbances in the model, an adaptive predictive control method is proposed based on recursive closed-loop subspace identification. A closed-loop subspace predictive control algorithm is constructed by making improvements to the closed-loop subspace predictive control algorithm through the incorporation of a PID-type objective function. This proposed algorithm is implemented online using a recursive algorithm, with fixed-size input and output data, and a simple, direct update method, which replaces LQ decompositions, for improving computational efficiency. Simulations prove this closed-loop subspace control algorithm to be efficient, predictive, and adaptive.
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出版历程
  • 收稿日期:  2014-03-25
  • 发布日期:  2015-04-19

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